Christoph Helma

Molecular feature mining in HIV data

We present the application of Feature Mining techniques to the Developmental Therapeutics Programs AIDS antiviral screen database. The database consists of 43576 compounds, which were measured for their capability to protect human cells from HIV-1 infection. According to these measurements, the compounds were classified as either active, moderately active or inactive. The distribution of classes is extremely skewed: Only 1.3 % of the molecules is known to be active, and 2.7 % is known to be moderately active.Given this database, we were interested in molecular substructures (i.e., features) that are frequent in the active molecules, and infrequent in the inactives. In data mining terms, we focused on features with a minimum support in active compounds and a maximum support in inactive compounds. We analyzed the database using the levelwise version space algorithm that forms the basis of the inductive query and database system MOLFEA (Molecular Feature Miner). Within this framework, it is possible to declaratively specify the features of interest, such as the frequency of features on (possibly different) datasets as well as on the generality and syntax of them. Assuming that the detected substructures are causally related to biochemical mechanisms, it should be possible to facilitate the development of new pharmaceuticals with improved activities.

Genotoxic effects of heavy metals: Comparative investigation with plant bioassays

The potential use of micronucleus assays in plants for the detection of genotoxic effects of heavy‐metal ions was investigated. Three different plant systems were comparatively investigated in micronucleus with Tradescantia pollen mother cells (Trad MCN) and micronucleus tests with meristematic root tip cells of Allium cepa and Vicia faba (Allium/MCN).

A public domain image-analysis program for the single-cell gel-electrophoresis (comet) assay

The single-cell gel electrophoresis (or comet) assay has gained widespread acceptance as a cheap and simple genotoxicity test, but it requires a computer-assisted image-analysis system. As commercial programs are expensive and inflexible, we decided to develop an image-analysis system based on public domain programs and make it publicly available for the scientific community. Our system is based on the scientific image-processing program NIH Image, and was written in its Pascal-like macro language. User interaction was kept as simple as possible, to enable the measurement of a large number of cells with a few keystrokes. Therefore, the time for image analysis is very low, even on slow computers. The comet macro can be obtained from http://mailbox.univie.ac.at/christoph.helma++ +/comet/, NIH Image is available at http://rsb.info.nih.gov/nih-image/. Both programs are free of charge.

Evaluation of the single cell gel electrophoresis assay with human hepatoma (Hep G2) cells

Human Hep G2 cells have retained the activities of phase I and phase II enzymes which are involved in the metabolism of environmental genotoxins. The present study describes the results of single cell gel electrophoresis (SCGE) assays with a panel of different model compounds with these cells. With genotoxic carcinogens such as aflatoxin B(1) (AFB(1)), benzo(a)pyrene (B(a)P), nitrosodimethylamine (NDMA) and cyclophosphamide (CP), statistically significant dose dependent induction of DNA migration was measured. With the two heterocyclic amines, 2-amino-3-methyl-3H-imidazo[4, 5-f]quinoline (IQ) and 3-amino-1,4-dimethyl-5H-pyrido[4,3-b]indole (Trp-P-1), and also with rodent carcinogens such as safrole, hexamethylphosphoramide (HMPA) and the pyrrolizidine alkaloid isatidine, which give negative results in other in vitro genotoxicity tests, positive results were obtained in Hep G2/SCGE assays. Nitrosomethylurea (NMU) was the only directly acting compound tested in the study and was by far (ca. 10(3)-fold) more active than the corresponding nitrosamine. The exposure concentrations required to cause significant effects varied over a broad range. The most pronounced effect was seen with AFB(1) (0.008 microM) followed by HMPA (15 microM), B(a)P (25 microM), NMU (100 microM), isatidin (500 microM), CP (900 microM), IQ (1200 microM), safrol (4000 microM), and NDMA (90x10(3) microM). Numbers in parenthesis give the lowest concentrations, which caused a significant increase of DNA migration. With two compounds, namely, the non-carcinogen pyrene and the synthetic hormone tamoxifen (TF), negative results were obtained under all test conditions. These findings are in agreement with the results of recent investigations which indicated that human hepatocytes are unable to convert TF to DNA reactive metabolites, whereas it is activated by rat liver cells and causes DNA adducts in these cells. Comparisons of the present results with data from earlier experiments indicate that the Hep G2/SCGE assay enables the detection of genotoxins including compounds which give misleading results in other in vitro genotoxicity tests and appears to be an alternative to tests with primary liver cells from laboratory animals.

The Predictive Toxicology Challenge 2000–2001

We initiated the Predictive Toxicology Challenge (PTC) to stimulate the development of advanced SAR techniques for predictive toxicology models. The goal of this challenge is to predict the rodent carcinogenicity of new compounds based on the experimental results of the US National Toxicology Program (NTP). Submissions will be evaluated on quantitative and qualitative scales to select the most predictive models and those with the highest toxicological relevance. Availability: http://www.informatik.uni-freiburg.de/∼ml/ptc/ Contact: helma@informatik.uni-freiburg.de.

Single-cell gel electrophoresis assays with human-derived hepatoma (Hep G2) cells.

The purpose of the present study was the development of a protocol for detecting chemically-induced DNA damage, using the alkaline single-cell gel electrophoresis (SCGE) assay with human-derived, metabolically competent hepatoma (Hep G2) cells. Previous studies indicated that Hep G2 cells have retained the activities of certain phase I and phase II enzymes and reflect the metabolism of genotoxins in mammals better than other in vitro models which require addition of exogenous activation mixtures. The optimal trypsin concentration for the removal of the cells from the plates were found to be 0.1%. Dimethylsulfoxide, at concentrations up to 2%, was an appropriate solvent for water-insoluble compounds. To determine the optimal exposure periods for mutagen treatment, the time kinetics of comet formation was investigated with genotoxic chemicals representing various classes of promutagens namely benzo[a]pyrene (B[a]P), 2-amino-3-methylimidazo[4,5-f]quinoline (IQ), and N-nitrosodimethylamine (NDMA) and with N-nitrosomethylurea (NMU). All compounds caused a statistically significant induction in DNA damage. With the promutagens, comet formation increased gradually as a function of the exposure duration, and reached maximum values between 20-24 h. With NMU, comet induction maximized already after a short exposure (1 h) and remained at a constant level for up to 24 h. Based on these results, the Hep G2/SCGE assay appears to be a suitable approach for investigating DNA damaging potential of chemicals. Further experiments with IQ and B[a]P showed that the assays are highly reproducible. Comparisons of the present results with those from earlier experiments in which other endpoints (induction of sister chromatid exchanges, micronuclei and chromosomal aberrations) were measured in Hep G2 cells, indicated that the sensitivity of the SCGE assays is more or less identical. Since the SCGE assay is less time consuming than other genotoxicity assays we anticipate that it might be a suitable approach to investigate DNA damaging effects of chemicals in the human-derived, metabolically competent cell line.

Collaborative development of predictive toxicology applications

OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH-relevant endpoints: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset. Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way.

Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001

MOTIVATION The development of in silico models to predict chemical carcinogenesis from molecular structure would help greatly to prevent environmentally caused cancers. The Predictive Toxicology Challenge (PTC) competition was organized to test the state-of-the-art in applying machine learning to form such predictive models. RESULTS Fourteen machine learning groups generated 111 models. The use of Receiver Operating Characteristic (ROC) space allowed the models to be uniformly compared regardless of the error cost function. We developed a statistical method to test if a model performs significantly better than random in ROC space. Using this test as criteria five models performed better than random guessing at a significance level p of 0.05 (not corrected for multiple testing). Statistically the best predictor was the Viniti model for female mice, with p value below 0.002. The toxicologically most interesting models were Leuven2 for male mice, and Kwansei for female rats. These models performed well in the statistical analysis and they are in the middle of ROC space, i.e. distant from extreme cost assumptions. These predictive models were also independently judged by domain experts to be among the three most interesting, and are believed to include a small but significant amount of empirically learned toxicological knowledge. AVAILABILITY PTC details and data can be found at: http://www.predictive-toxicology.org/ptc/.

A survey of the Predictive Toxicology Challenge 2000-2001

MOTIVATION The Predictive Toxicology Challenge (PTC) was initiated to stimulate the development of advanced techniques for predictive toxicology models. The goal of this challenge was to compare different approaches for the prediction of rodent carcinogenicity, based on the experimental results of the US National Toxicology Program (NTP). RESULTS 111 sets of predictions for 185 compounds have been evaluated on quantitative and qualitative scales to select the most predictive models and those with the highest toxicological relevance. The accuracy of the submitted predictions was between 25 and 79 %. An evaluation of the most accurate models by toxicological experts showed, that it is still hard for domain experts to interpret the submitted models and to put them into relation with toxicological knowledge. AVAILABILITY PTC details and data can be found at: http://www.predictive-toxicology.org/ptc/.

lazar: a modular predictive toxicology framework

lazar (lazy structure–activity relationships) is a modular framework for predictive toxicology. Similar to the read across procedure in toxicological risk assessment, lazar creates local QSAR (quantitative structure–activity relationship) models for each compound to be predicted. Model developers can choose between a large variety of algorithms for descriptor calculation and selection, chemical similarity indices, and model building. This paper presents a high level description of the lazar framework and discusses the performance of example classification and regression models.